Additional illustrations of NL-SAR method for resolution-preserving (Pol)(In)SAR denoising

This document provides additional information and results of the method NL-SAR described in our paper: "NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising" submitted to IEEE Trans. on Geoscience and Remote Sensing [Deledalle et al., 2013]. NL-SAR is a fully automatic method for speckle reduction that handles amplitude, polarimetric and/or interferometric SAR data. It can process single look and multi-look images. The source code of the method is freely available at: http://www.math.u-bordeaux1.fr/cdeledal/nlsar.php

Multidimensional and temporal SAR data representation and processing based on binary partition trees

This thesis deals with the processing of different types of multidimensional SAR data for distinct applications. Instead of handling the original pixels of the image, which correspond to very local information and are strongly contaminated by speckle noise, a region-based and multiscale data abstraction is defined, the Binary Partition Tree (BPT). In this representation, each region stands for an homogeneous area of the data, grouping pixels with similar properties and making easier its interpretation and processing. The work presented in this thesis concerns the definition of the BPT structures for Polarimetric SAR (PolSAR) images and also for temporal series of SAR acquisitions. It covers the description of the corresponding data models and the algorithms for BPT construction and its exploitation. Particular attention has been paid to the speckle filtering application. The proposed technique has proven to achieve arbitrarily large regions over homogeneous areas while also preserving the spatial resolution and the small details of the original data. As a consequence, this approach has demonstrated an improvement in the performance of the target response estimation with respect to other speckle filtering techniques. Moreover, due to the flexibility and convenience of this representation, it has been employed for other applications as scene segmentation and classification. The processing of SAR time series has also been addressed, proposing different approaches for dealing with the temporal information of the data, resulting into distinct BPT abstractions. These representations have allowed the development of speckle filtering techniques in the spatial and temporal domains and also the improvement and the definition of additional methods for classification and temporal change detection and characterization

Mathematical Morphology on the Sphere: Application to Polarimetric Image processing

Projecte final de carrera fet en col.laboració amb Centre de morphologie mathématique, École des Mines de ParisEnglish: The fully polarimetric synthetic aperture radar (PolSAR) provides data containing the complete scattering information. Therefore, these data have drawn more attention in recent years. PolSAR data can be represented as polarization states on a sphere. We present image processing techniques based on the analysis of the polarimetric information within its location on the sphere. Mathematical morphology is a well-known nonlinear approach for image processing. It is based on the computation of minimum and maximum values of local neighborhoods. That necessitates the existence of an ordering relationship between the points to be treated. The lack of a natural ordering on the sphere presents an inherent problem when defining morphological operators extended to unit sphere. We analyze in this project some proposals to the problem of ordering on the unit sphere, leading to formulations of morphological operators suited to the configuration of the data. The notion of local supremum and infimum is introduced, which allows to define the dilation and erosion on the sphere. Supervised orderings are considered and its associated operators for target recognition issues. We also present various filtering procedures for denoising purposes. The diferent methods studied in this project pursuit the generalization of the morphological operators on the sphere. Through the analysis performed, we pretend to achieve an understanding of the data and automation of the target detection.Castellano: El radar de apertura sintética totalmente polarimétrico (PolSAR) proporciona datos que contienen la información completa de dispersión. Estos datos han captado más atención en los últimos años. Los datos PolSAR pueden ser representados como estados de polarización en una esfera. Se presentan las técnicas de procesamiento de imágenes basadas en el análisis de la información polarimétrica y en su ubicación en la esfera. La morfología matemática es una técnica no lineal para el procesamiento de imágenes. Se basa en el cálculo de los valores mínimos y máximos alrededor de un punto. Precisa de la existencia de una relación de orden entre los puntos a tratar. La falta de un orden natural en la esfera presenta un problema inherente a la hora de definir los operadores morfológicos extendidos a la esfera unidad. En este proyecto se analizan algunas propuestas para el problema del orden en la esfera unidad, lo que da lugar a formulaciones de los operadores morfológicos adaptados a la configuración de los datos. Se introduce la noción de supremo e ínfimo local, lo que permite definir la dilatación y la erosión en la esfera. Consideramos órdenes supervisados y sus operadores asociados para problemas de reconocimiento de objetivos. También se presentan varios procedimientos de filtrado para la eliminación de ruido. Los diferentes métodos estudiados en este proyecto persiguen la generalización de los operadores morfológicos a la esfera. A través del análisis realizado, se pretende lograr una comprensión de los datos y la autCatalà: El radar d'obertura sintètica totalment polarimètric (PolSAR) proporciona dades que contenen la informació completa de dispersió. Aquestes dades han captat més atenció en els últims anys. Les dades PolSAR poden ser representades com a estats de polarizació en una esfera. Es presenten tècniques de processament d'imatge basades en l'anàlisi de la informació polarimètrica i en la seva ubicació en l'esfera. La morfologia matemàtica és una tècnica no lineal per al processament d'imatges. Es basa en el càlcul dels valors mínim i màxim al voltant d'un punt. Precisa de l'existència d'una relació d'ordre entre els punts a tractar. La manca d'un ordre natural en l'esfera presenta un problema inherent a l'hora de definir els operadors morfològics estesos a l'esfera unitat. En aquest projecte s'analitzen algunes propostes per al problema de l'ordre en l'esfera unitat, el que dóna lloc a formulacions dels operadors morfològics adaptats a la configuració de les dades. S'introdueix la noció de suprem i mínim local, el que permet definir la dilatació i l'erosió en l'esfera. Considerem ordres supervisats i els seus operadors associats per a problemes de reconeixement d'objectius. També es presenten diversos procediments de filtratge per a la eliminació de soroll. Els diferents mètodes estudiats en aquest projecte busquen la generalizació dels operadors morfològics a l'esfera. Mitjançcant l'anàlisi realitzat, es pretén aconseguir la comprensió de les dades i l'a

A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging.

As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conventional pixel-based 1-D SSA fails to produce satisfactory results, while the band-image-based 2D-SSA is also infeasible especially for the popularly used line-scan mode. To tackle these challenges, in this article, a novel 1.5D-SSA approach is proposed for in situ spectral-spatial feature extraction in HSI, where pixels from a small window are used as spatial information. For each sequentially acquired pixel, similar pixels are located from a window centered at the pixel to form an extended trajectory matrix for feature extraction. Classification results on two well-known benchmark HSI datasets and an actual urban scene dataset have demonstrated that the proposed 1.5D-SSA achieves the superior performance compared with several state-of-the-art spectral and spatial methods. In addition, the near real-time implementation in aligning to the HSI acquisition process can meet the requirement of online image analysis for more efficient feature extraction than the conventional offline workflow

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

SAR Remote Sensing of Canadian Coastal Waters using Total Variation Optimization Segmentation Approaches

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure